www.sciencemag.org/content/350/6265/1248/suppl/DC1
Supplementary Materials for
Local flow regulation and irrigation raise global human water consumption and footprint
Fernando Jaramillo* and Georgia Destouni
*Corresponding author. E-mail: [email protected]
Published 4 December 2015, Science 350, 1248 (2015) DOI: 10.1126/science.aad1010
This PDF file includes:
Materials and Methods Figs. S1 to S7 Tables S1 to S4 References (33–62)
This PDF file includes:
Materials and Methods: ....................................................................................................... 2
Hydroclimatic changes ........................................................................................................... 2
Flow regulation and irrigation (FRI) developments ............................................................... 4
Statistics and significance ....................................................................................................... 4
FRI-related global freshwater consumption ........................................................................... 6
Method 1 ............................................................................................................................. 7
Method 2 ............................................................................................................................. 8
Global freshwater consumption .......................................................................................... 8
Account for FRI-related changes in water storage .............................................................. 9
Discussion of physical behavior-representativeness of NA basins for the global scale ....... 10
Figures S1-S7 ...................................................................................................................... 13
Tables S1-S4 ........................................................................................................................ 21
Submitted Manuscript: Confidential October 31, 2015
Materials and Methods:
Hydroclimatic changes
We evaluate inter- and intra-annual changes in the terrestrial freshwater system (as conceptualized in Fig. S1) by studying hydroclimatic time series of observed precipitation (P) (33), runoff (R) (34) and temperature (T) (33) during the period 1901-2008 (Table S1). We depart from observed changes as the difference between the annual means of P and R in the sub-periods 1901-1954 and 1955-2008. Similar comparative sub-periods have also been previously used in recent hydroclimatic change assessments (5,35) and should be relevant and adequate for studying flow regulation and irrigation (FRI) effects on freshwater changes because the cumulative volume of reservoir impoundment in all continents increased considerably around 1955 (36), with the peak of reservoir construction, regarding both volume and number of FRI projects, being in the 1960s (37). Irrigated area has also increased by around 300% since 1950 (38).
In consistency with other studies of historic hydroclimatic changes (5,35), we analyze freshwater changes between periods with at least 10 years of complete R data within each of the total comparative periods 1901-1954 and 1955-2008. The reason for this 10-year choice is to cover a sufficiently long climatic period while also optimizing the global statistical basis of basin-wise hydroclimatic change assessment (38), including the number and geographical representation of hydrological basins used in the global assessment and the share of significant change results as basis for that assessment (Fig. S2). A sensitivity analysis of using different number of years required around the main choice of minimum 10 years of complete R data within each comparative period is shown in Table S2.
Among the hydroclimatic variables, the R data is generally limiting with regard to time length and completeness of data availability. We consider here R data for a year to be complete for a hydrological station when it is available for all 12 months of the year (for monthly R data) or for at least 98% of the days of the year (for daily R data). For years with less than 100% (but still at least 98%) availability of daily R data, we complete the time series to full year coverage by linear interpolation.
For the present analysis of possible detectable FRI-effect signals in observed or data-given hydroclimatic changes, we only consider the hydrological basins of the largest river systems on Earth which were previously used to categorize the degree of impact of FRI at the global scale (7,11). This impact is grouped in three FRI-impact level categories of basins: Non-affected (NA), Moderately affected (MA) and Strongly affected (SA).
The FRI-impact categorization relates to the total drainage area of various large basins that feed into each categorized river system (7,11); different parts of a large river system are by definition associated with different basins and we consider here only non-overlapping basins. We thus use the FRI-impact categorization of each river system for all of the large, non-overlapping basins that drain into that river system and that fulfill the data criteria. For FRI-categorized basins where R data availability does not fulfill the associated criteria for the whole basin scale, we consider instead the largest possible (sub)basin area fulfilling the R data criteria within that main basin, so that the FRI-impact categorization of
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the latter agrees as much as possible with the hydroclimatic information available for the (sub)basin within it. In total, 100 hydrological basins (including large (sub)basins within main FRI-categorized basins), spanning over all continents and latitudes are included in the present analysis; among these, 17 are categorized as NA, 30 as MA and 53 as SA (Fig. 1). There is at least one basin of each category in every continent, with the exception of Oceania that has only two (2) basins satisfying the selection criteria. The area-weighted means of latitudinal outlet location of the investigated basins within each FRI category are 47.6°N, 21.6°N and 30.0°N, showing that most basins that comply with the R data criteria are concentrated in the Northern Hemisphere.
Monthly or daily R data for the selected 100 hydrological basins are compiled from The Global Runoff Data Centre (34) along with corresponding basin shapefiles and basin area data. They are temporally aggregated to obtain annual R. Gridded data of monthly P and T are obtained for each basin by masking the gridded data with its corresponding basin shapefile and using a general WGS1984 spherical projection for this task. Area-weighted means of P and T are temporally aggregated (for P) or averaged (for T) to annual values for each basin from the values for all cells included within the basin’s shape file boundary.
Since there is commonly no direct observation data of actual evapotranspiration (AET) available on whole large basin scales and for the long period here studied, we calculate for each individual basin a water balance-constrained estimate of apparent actual evapotranspiration (AET) as
RPAET (Eq. 1)
We use here the term “apparent” for the AET estimation from Eq. (1) because this estimate may in reality represent the sum (AET+ΔS) of real annual average AET and a possible nonzero annual average change in water storage (ΔS). However, it is a well-established approach to estimate long-term average (apparent) AET and its change ΔAET from data-given long-term average P and R and their changes ΔP and ΔR, respectively, under the assumption of essentially zero long-term average water storage change, ΔS≈0, on hydrological basin scales; this has been done in many previous papers, e.g., (3) and multiple references within. For long historic climate periods, like those investigated here, deviations from long-term average ΔS≈0 may be relatively small compared to other water balance terms (6,39) . For example, reported multiple reanalysis data products for the Congo and Upper Blue Nile basins mostly indicate ΔS terms of <3mm/yr, to be compared with precipitation terms in excess of 1,500 mm/yr (40). It is therefore relevant to depart from an assumption of long-term average ΔS≈0 for a base-case estimation of long-term average annual AET by using Eq. 1.
Nevertheless, long-term average ΔS will be non-zero (ΔS≠0) during some water storage shifts that may occur within a basin, for instance due to the water filling of reservoirs after construction, systematic consumptive (surface or ground) water removal for human use (41), or climate-driven phase changes such as glacier melting (42) or permafrost thaw (43) that decrease the storage of frozen water with at least some of the phase-changed
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liquid water adding to R rather than to (surface or ground) water storage. For this reason, we also quantify in connection with the calculations of global FRI-related ΔAET and associated freshwater consumption some reasonable FRI-related effects of ΔS≠0, as described further below.
In general, the assessment of long-term average AET in each basin and period of the present study is limited by the temporal availability of R data required for the AET calculation. Hence, in the calculation of period-average values of all hydroclimatic parameters considered in this study, only years with available R data within the comparative periods 1901-1954 and 1955-2008 are taken into account. Additionally, we use the annual data on T, P, R and AET to calculate freshwater changes in terms of potential evapotranspiration (PET) with the Langbein model (44), PET relative to P (PET/P), AET relative to P (AET/P), relative intra-annual variability of monthly runoff (CVR) and relative intra-annual variability of monthly precipitation (CVP). These coefficients of variation CVR and CVP are calculated annually as the standard deviation of monthly R and P values, respectively, relative to the mean monthly value of R and P, respectively, within each year.
Flow regulation and irrigation (FRI) developments
In order to distinguish the possible effects of flow regulation on global freshwater changes between the periods 1901-1954 and 1955-2008, we quantify the change between these periods in total reservoir storage capacity (9) relative to basin area (RES) from the Global Reservoir and Dam (GRanD) database (9) (Figs. 3b and 4b). This database includes 2,556 dams with storage capacity of more than 0.1 km3 that have been established in the 100 investigation basins; by established we mean either constructed, completed, commissioned, refurbished or updated. Of this total number, 1,146 dams have been established during the period 1901-1954, 1,267 during the period 1955-2008, and for 143 dams the date of establishment is not specified or unknown. Hence, for the present assessment of FRI-related freshwater change between the periods 1901-1954 and 1955-2008, we compare the data-given hydroclimatic changes with the change in RES implied by the dams established during the second period 1955-2008.
For assessment of irrigation effects on global freshwater change, worldwide information on irrigation evolution is only available at country level since 1960 (45). Hence, we use here the current relative area equipped for irrigation (in 2005), as obtained for the 100 basins from the digital global map of irrigated areas (10), to estimate the development of irrigated relative to total basin area (IA ) from the period 1901-1954 to the recent period 1955-2008 and with the IA value in 2005 as an end point.
Statistics and significance
The period-average value of each hydroclimatic variable x (P, R, AET, AET/P, PET/P, CVR, CVP) within each basin over the period PD=1 (1901-1954) or PD=2 (1955-2008) is calculated as
Submitted Manuscript: Confidential October 31, 2015
M
x
x
M
i
i
PD
1 (Eq. 2)
where xi is the annual mean variable value in year i within period PD and M is the total number of years i with complete R data in period PD. The temporal change from period 1 to period 2 (Δx) in each hydroclimatic variable (i.e., ΔP, ΔR, ΔAET, ΔAET/P, ΔPET/P, ΔCVR, ΔCVP) is further represented as
12Δ xxx (Eq. 3)
For the temporal change in each hydroclimatic variable for each FRI-impact level category (i.e., NA, MA and SA), the arithmetic mean (µ; Figs. 3-4, Fig. S3) and the area-weighted mean (µ*; Fig. 2) are calculated as
N
x
μ
N
j
j
1 (Eq. 4)
N
j
j
N
j
jj
*
A
xA
μ
1
1 (Eq. 5)
where Aj is the area of each basin j and N is the total number of studied basins in each FRI-impact level category or in each of the five 20-basin subgroups (G1, G2, G3, G4 and G5) in which the distributions of ΔAET/P (Fig. 3) and ΔCVR (Fig. 4) are divided. To also assess the spatial variability of change Δx among the basins in each FRI-impact level category, an area-weighted spatial standard deviation (*) is calculated (46) (Fig. 2)
N
j
N
j
j
j*
A
Axσ
1
2
1
Δvar (Eq. 6)
where var(Δx) is the spatial variance of Δx among the basins in each FRI-impact category
N
μx
x
N
j
j
1
2ΔΔvar (Eq. 7)
Distribution statistics of µ, median, outliers, interquartile range IQR as the 1st quartile, Q25, subtracted from the 3rd quartile, Q75, and whiskers representing a confidence interval of NIQR58.1 , are calculated for ΔAET/P, ΔPET/P, ΔCVR, ΔCVP in each FRI-impact level category (Figs. S3). For RES, IA, PET/P, PET/P, CVP and ΔCVP, these
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statistics are also calculated for the basins in each of the five 20-basin subgroups (G1, G2, G3, G4 and G5) in which the distributions of ΔAET/P (Fig. 3) and ΔCVR (Fig. 4) are divided.
Furthermore, we perform a one-sided unpaired Wilcox rank sum test (or Mann-Whitney test) to assess change differences in the distributions of AET/P, PET/P, CVR and CVP among the FRI-impact categories considering significance at the 95% significance level, i.e., p<0.05 (Table S2). The null hypothesis used is that there is no location shift in the distributions of each parameter between any two FRI-impact categories (NA, MA and SA; Table S2-S3) or between subgroups (Table S4). The one-sided alternative hypotheses is based on the results found in this study of increasing AET/P with
increasing FRI-impact level and decreasing CVR with increasing FRI-impact level (Fig. 2). These results agree with previous corresponding findings at regional scales for AET/P (6,13,14,47) and CVR (6,14,15,48,49). As such, we test if an increase in RES, IA, PET/P
or PET/P explains AET/P as it increases from G1 to G5 (Fig. 3), or if an increase in RES, IA, CVP and CVP explains CVR as it decreases from G5 to G1 (Fig. 4).
FRI-related global freshwater consumption
Human freshwater consumption refers to human-driven increase in AET, which implies a corresponding human-driven increase in the loss of freshwater from the landscape to the atmosphere (1). Consequently, we quantify here FRI-related freshwater consumption from our estimations of human-driven FRI-related increase in total volumetric long-term annual average AET between the periods 1901-1954 and 1955-2008. To obtain an observation-based estimate of average FRI-related increase in AET, we calculate first the area-normalized average AET (Eq. 5) in the MA and in the SA basins, and subtract for each of these FRI-affected basin categories the area-normalized non-FRI-related average AET in the NA basins (Eq. 5). The assumption underlying this calculation is that, on average across all the different world conditions that are spanned by the basins within each classification category, the world-average change AET for each basin category can be assumed to be approximately similar among the categories, except for the FRI-related effects that constitute the very basis for distinguishing the three basin categories in the first place.
The assumption that this category distinction is meaningful (in terms of revealing considerably different FRI-related changes between the three FRI effect-based categories) is supported by the distinctly different category-results in Fig. 2 and in the FRI-related panels in Figs. 3-4. The assumption of non-FRI-related conditions and changes being on average similar across the basins of all categories is also supported by the results for non-FRI-related variables, like PET/P and PET/P (associated panels in Fig. 3) and CVP and CVP (associated panels in Fig. 4). In combination, these results support the relevance, under the prevailing data limitations, of estimating average FRI-related change per unit basin area by subtracting an approximate average non-FRI-related change per unit basin area (as obtained across the non-affected basins in different parts of the world) from the total average change per unit basin area for each of the FRI-affected categories.
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However, we also recognize that sufficiently monitored NA basins are found to greater degree in the Northern hemisphere at medium to high latitudes than the FRI-affected MA and SA basins, which are distributed more evenly among latitudes and continents (Fig. 1). To also address this distribution difference, we estimate here FRI-related change in AET with two different methods. Method 1 considers total AET whereas Method 2 follows a previously developed and applied approach ((5) and references therein) to distinguish two different components of the total AET (see also the conceptualization in Fig. S1). The first component is associated with the observed atmospheric climate change in P and T, which is expected to differ most among basins in different world locations and is removed from total AET, and a remaining part that is more associated with landscape-driven changes (including FRI) and is denoted LET. In synthesis, our final estimates of FRI-related global AET and associated global human freshwater consumption are based on the average of the results of Methods 1 and 2, and the uncertainty range given by the estimate difference between the methods.
We further also consider in the final estimate of global FRI-related ΔAET and freshwater consumption some FRI-related changes in water storage (ΔS≠0). These include the FRI-related water storage increase implied by the water filling of reservoirs to their average water level that is thereafter maintained by the reservoir management. In addition, we also consider the groundwater depletion that may occur when unmanaged groundwater storage is used for irrigation in areas around the world that, for various reasons, do not have access to managed surface water storage and therefore do use groundwater for irrigation. This groundwater use can be expected to be mostly consumptive and thus deplete groundwater storage because most of the water amount used for irrigation (whether surface water or groundwater) has been found to feed into increased AET from irrigated areas under wide-ranging climate conditions; see (6) and references therein (13,50-51).
Finally, we elaborate and discuss further below some considerations on the physical behavior of NA (as well as MA and SA) basins, in order to address the representativeness of non-FRI-related AET changes (besides those driven by atmospheric climate change that are handled in Method 2) across different parts of the world. In particular, we discuss the representativeness of non-FRI changes in the investigated NA basins for corresponding non-FRI changes occurring in the other basin categories.
Method 1
The average area-normalized AET change for basins in each FRI-impact level category (AETNA, AETMA and AETSA, respectively) is obtained from Eq.5 with the following values: -29 mm/yr for NA basins, 3 mm/yr for MA basins and 8 mm/yr for SA basins. We then calculate the average FRI-related AET change (in mm/yr) in each of the FRI-affected categories (AETFRI-MA and AETFRI-SA) as
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NAMAMAFRI AETAETAET (Eq. 8)
NASASAFRI AETAETAET (Eq. 9)
The FRI-related change in total volumetric AET-flow for each FRI-affected category (AETVOL-FRI-MA and AETVOL-FRI-SA) is further estimated by multiplying each average area-normalized FRI-related AET change by the corresponding total area of basins in each category (AMA= 20,695,803 km2 and ASA= 22,578,466 km2) as
yrkmAAET
AET MAMAFRIMAFRIVOL
3667
000,000,1*
(Eq. 10)
yrkmAAET
AET SASAFRISAFRIVOL
3845
000,000,1*
(Eq. 11)
The total volumetric FRI-related AET change over the total land area covered by all of the 100 basins of the three FRI-impact categories (ANA-MA-SA= 45,296,318 km2) is then estimated as the sum of Eq. 10 and 11 with the assumption of small average FRI-related change in the NA basins. This yields a total volumetric AETVOL-FRI of 1,512 km3/yr.
Method 2
The landscape-driven component LET of the total AET is estimated as (5)
EETAETLET (Eq. 12)
where ΔEET estimates the atmospheric climate-driven component of AET based on the observed data for P and T and their implication for PET, based on the Langbein model (44). The P and PET data and results for each basin and period 1901-1954 and 1955-2008 are used in six different empirical relations for EET estimation (52-57), as previously explained and outlined in detail in (5), and ΔEET for each basin is estimated as the average result of the five methods. The resulting average ΔLET for each FRI-related basin category is then ΔLETNA= -16 mm/yr, ΔLETMA=-2 mm/yr and ΔLETSA=8 mm/yr. Furthermore, the estimated volumetric FRI-related change ΔLET becomes then (based on Eqs. 10-11 with ΔLET
instead of ΔAET): LETVOL-FRI-MA = 287 km3/yr for the MA basins, LETVOL-FRI-SA=545 km3/yr for the SA basins, and in total LETVOL-FRI=832 km3/yr for all 100 basins.
Global freshwater consumption
The global implications of the results for the 100 study basins for FRI-related AET change and associated FRI-related human freshwater consumption are estimated by area-based extrapolation to the total world land area excluding Antarctica (Aw= 130,499,725 km2). This yields 4,355 km3/yr for global AETVOL-FRI from Method 1, 2,396 km3/yr for global
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LETVOL-FRI from Method 2, and in combination for the two methods a global AET and associated global freshwater consumption of 3,376 ± 979 km3/yr.
Adding to this estimate of global FRI-related freshwater consumption the so far relatively uncontested and previously estimated human freshwater consumption for other sectors (from summary in (1): 3,628 km3/yr for non-irrigated agriculture (6) minus 3,000 km3/yr for deforestation (58) plus 179 km3/yr for the industrial and municipal sectors (59)) yields a total global human consumption of freshwater of 4,183 ± 979 km3/yr.
Account for FRI-related changes in water storage
A change in long-term average apparent AET based on Eq. 1 may tacitly include change S) in the long-term average annual S from its expected near-zero value within each basin. For non-zero S), the real (rather than the apparent) AET can be obtained from the full water balance-based change equation
SRPAET (Eq.13)
For any data-given water flux changes P and R, a change S) from average S≈0 in the first period 1901-1954 to positive (negative) average S≠0 in the second period 1955-2008 implies a smaller (greater) increase in real AET based on Eq. 13 than in apparent AET based on Eq. 1.
The water filling of reservoirs after construction implies a FRI-related increase in
average water storage from 1901-1954 to 1955-2008. Based on the total reservoir water volume Vr established over 1955-2008 in the 100 investigated basins (Table S1) and on an average reservoir water level corresponding of about 85% of Vr (since the reservoir water level does and must be able to fluctuate around its average filled stage), the average reservoir-related Sr over the 54-year period of 1955-2008 can be estimated as Sr = 0.85 * Vr / M, where M=54. For the investigated 100 basins with total Vr / M ≈ 35 km3/yr over 1955-2008 Sr yields approximately 29 km3/yr. Area-based extrapolation to the world land area excluding Antarctica further yields average global Sr ≈ 85 km3/yr and thus an associated change from 1901-1954 to 1955-2008 of Sr) = 85 – 0 = 85 km3/yr.
Furthermore, direct groundwater use for irrigation purposes can be expected to be mostly consumptive and thus deplete groundwater (41,60-62) since most of the water amount used for irrigation (whether it is surface water or groundwater) has been found to feed into increased AET from irrigated areas under a wide range of climate conditions; see (6) and references therein (13,50-51). In order to also account for such FRI-related global groundwater depletion (implying change to negative average S in 1955-2008), we consider a reported estimate of current global groundwater use for irrigation of about 545 km3/yr (26); other studies (62) report such additional global estimates that may be used to further analyze uncertainties associated with the global groundwater use for irrigation. However, for the present study application this uncertainty is well within the estimated uncertainty range ± 979 km3/yr of global FRI-related water consumption as evident from the following quantification of expected groundwater depletion effect.
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A current global groundwater use of 545 km3/yr implies a temporal average annual groundwater use of about 272 km3/yr over the whole period 1955-2008 ((0 + 545)/2). The associated temporal average groundwater depletion (storage decrease) over the whole period 1955-2008 can then be estimated as Sgw = - 272 km3/yr with associated change from 1901-1954 to 1955-2008 of Sgw) = - 272 – 0 = - 272 km3/yr.
In combination, the total global storage change effect of FRI-related reservoir filling and groundwater depletion is then S) = Sr) + Sgw) = 85 – 272 = -187 km3/yr. From this and the estimated apparent global AETA of 3,376 ± 979 km3/yr, a real FRI-related global AET and associated global freshwater consumption is then obtained as AET = 3,376 - -187 = 3,563 ± 979 km3/yr. Furthermore, the total global freshwater consumption is analogously increased to 4,370 ± 979 km3/yr (Fig. S6).
Discussion of physical behavior-representativeness of NA basins for the global scale
The globe has only few large NA river basins left and even fewer that have the long-term hydroclimatic data needed for the present analysis. Many of the present NA basins with such data availability are located at latitudes above 40oN, whereas MA and SA basins with required data availability are more evenly distributed among continents and latitudes. We therefore elaborate here further on the physical behavior of the 17 NA basins (and also MA and SA), with regard to their representativeness for basins located in other places around the world.
We use a tool developed, applied and published previously (5) for analyzing
characteristic movements of hydroclimatic change in Budyko space (in terms of AET/P and PET/P) among a large number of basins (Fig. S7). Budyko space (22) is a conceptualization space that relates AET/P changes to PET/P changes and links basic physical principles of water and energy availability that govern water balance at the basin scale. By application of this tool, we analyze and discuss characteristic multi-basin behaviors of hydroclimatic changes that may have occurred in NA, MA and SA basins around the world.
In summary, we represent hydroclimatic change resulting from both climate and
landscape drivers for each basin as a vector of movement in Budyko space of total hydroclimatic change magnitude
22
P
PET
P
AETm (Eq. 14)
and direction starting clockwise from the upward direction,
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P
PET
P
AET
b arctan (Eq. 15)
where b = 90° when ΔPET/P> 0 and b = 270° when ΔPET/P< 0.
The resulting movement vectors representing hydroclimatic change between 1901-1954 and 1955-2008 for all basins in each category are grouped and visualized in spectra resembling wind roses (Fig. S7). The spectra show how AET/P has decreased in 13 of 17 NA basins (76%), 18 of 30 MA basins (60%) and 20 of 53 SA basins (38%). In the present study, we subtract the area-normalized AET<0 in NA basins from the area-normalized AET in SA and MA basins to assess the FRI-related change-effects in the latter basins. It is therefore important to check if AET<0 (decrease in AET/P) is only characteristic of Northern NA basins or if it should be also expected for NA basins in other parts of the world. For the present NA basins, we see then that AET/P <0 occurs even in one of the NA basins closest to the Equator, the Rio Jequitinhonha basin in Brazil, and that in this basin and in the other NA tropical basin, the Chari river basin in central Africa, AET<0. Hence, the AET<0 result is not at all restricted to just Northern NA basins.
Furthermore, certain change directions within Budyko space are related with
atmospheric climate changes (EET), while landscape-driven changes (LET) are needed to also explain changes in other directions (5). Change directions that indicate dominant atmospheric climate drivers (45°<θ<90° or 225°<θ<270) are more frequent in NA basins (41%) than in MA (27%) or SA (28%) basins. Landscape drivers such as FRI are thus required to explain more of the changes occurring in the SA and MA basins. However, landscape drivers are also required to explain a majority of changes occurring in the NA basins. In the Northern NA basins, the frequent result of AET<0 may then be attributed to deforestation. However, deforestation has also occurred in NA basins closer to the Equator as it may have happened in the Rio Jequitinhonha and Chari river basins where LET<0 and in general in NA basins in the tropics (21,58), for which we can then also expect decreases in AET.
Moreover, NA basins exhibit lower absolute values of AET/P than the MA and SA
basins, as visible in the pattern of more horizontal change movements in Budyko space for NA basins compared to the more vertical change movements of MA and SA basins. This NA change pattern agrees with previous findings for unregulated basins in Sweden (6). Since basins at lower latitudes (e.g., near the Equator) constitute less energy-limited environments (PET/P>1) than basins at higher latitudes (PET/P<1), the AET/P changes in the former should generally be smaller than those in the latter for the same unit change in PET/P (especially in NA basins without major FRI effects to also push changes in other directions than along the theoretical Budyko curves (52-57)). Hence, we expect NA basins
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at lower latitudes to exhibit even smaller (i.e., more negative) AET/P changes than those observed in NA basins at higher latitudes.
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Figures S1-S7
Fig. S1. Changes to the freshwater system (light blue arrows) and their possible drivers in the atmosphere (dark blue, red, orange) and the landscape (green). Among the latter, the present study focuses on distinguishing effect signals of hydroclimatic change driven by flow regulation and irrigation (FRI) (violet).
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Figure S2. Sensitivity analysis regarding the basin requirement of minimum number of years with complete runoff (R) data in each period. The results show the number of non-affected (NA) basins (blue) and total basins (red) that fulfill the requirement for different minimum number of required R data years, along with the share of one-sided significant results (orange) regarding change differences between any of the NA, MA and SA basin categories (non-affected - NA, moderately affected - MA and strongly affected - SA), as listed and explained further in Tables S2 and S3. The black dashed vertical line shows the chosen minimum requirement of number of years with R data (10 years) selected for the present analysis.
0
20
40
60
80
100
120
2 4 6 8 10 12 14 16 18 20
Nu
mb
er
of
ba
sin
s a
nd
sh
are
o
f sig
nific
ant re
su
lts (
%)
Minimum number of complete years requirement
Number of NA basins
Number of total basins
Significant result share (%)
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Fig. S3. Box plots of the basin distributions within the three FRI-impact categories for changes in: (A) the ratio of actual evapotranspiration to precipitation (AET/P), (B) relative intra-annual variability of monthly runoff (CVR), (C) the ratio of potential evapotranspiration to precipitation (PET/P) and (D) the relative intra-annual variability of monthly runoff (CVP). Boxplot statistics include arithmetic mean (black crosses), median (thick horizontal black line), interquartile range (IQR) (boxes) and whiskers (confidence interval of NIQR58.1 ) and outliers (black circles). Results of statistical significance testing (p<0.05) by a one-sided unpaired Wilcox rank sum test for the differences between basin-categories (non-affected - NA, moderately affected - MA and strongly affected - SA) are listed in Table S3.
D C
D
B A
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Fig. S4. Scatter plots of changes in the ratio of actual evapotranspiration to precipitation (AET/P) between the periods 1910-1954 and 1955-2008 for the 100 large basins. Results are shown versus changes in total reservoir storage capacity relative to basin area between the two time periods (RES; in mm), area equipped for irrigation relative to basin area (IA), ratio of potential evapotranspiration to precipitation (PET/P) and change in PET/P, i.e. PET/P. The plots include a linear regression (red), its variation (R2) and p-value (red text).
A
Submitted Manuscript: Confidential October 31, 2015
Fig. S5. Scatter plots of changes in the relative intra-annual variability of monthly runoff (CVR) between the periods 1910-1954 and 1955-2008 for the 100 large basins. Results are shown versus changes in total reservoir storage capacity relative to basin area between the two time periods (RES; in mm), area equipped for irrigation relative to basin area (IA), relative intra-annual variability of monthly precipitation (CVP) and change in CVP, i.e., CVP. The plots are shown as in Fig. S4.
A
A
Submitted Manuscript: Confidential October 31, 2015
Figure S6. Estimate of global human freshwater consumption of 4,370 ± 979 km3/yr. Results distinguish the estimated consumption contributions of flow regulation and irrigation (FRI) when including the possible effects on change in water storage change (S) by systematic long-term groundwater depletion due to water use for irrigation (26) and flow regulation, as calculated here, and that of others sectors summarized in (1). The dashed horizontal line shows a proposed planetary boundary for consumptive use of freshwater (27).
628
179
3,563
0
1,000
2,000
3,000
4,000
5,000
6,000
Hu
ma
n fre
sh
wa
ter
co
nsu
mp
tio
n (
km
3/y
r)
Flow regulation andirrigation
Industrial and municipal
Net of deforestation andnon-irrigated agriculture
Submitted Manuscript: Confidential October 31, 2015
Figure S7. Spectra of the direction and magnitude of hydroclimatic change movements in Budyko space (5) from an initial point (star) for different basin categories: (A) non-affected - NA basins, (B) moderately affected - MA basins, and (C) strongly affected - SA basins. The changes are between the climatic periods 1901-1954 and 1955-2008 and their movements represented by a change vector with horizontal projection for the change in potential evapotranspiration relative to precipitation (PET/P) and vertical projection for the change in actual evapotranspiration relative to precipitation (AET/P). The vectors express the direction of change movement () (Eq. 15) and the magnitude of change movement (Eq. 14). The range of change directions 0°<<360° is divided in 15° interval-paddles that group all basins moving in each direction interval. Color intervals represent the fraction (%) of basins (from the total number of basins included in the spectrum) with a corresponding moving in that range and with a corresponding dimensionless magnitude (m; Eq. 14) between 0 and 0.01 (light color in each panel), between 0.01 and
A B
C
Submitted Manuscript: Confidential October 31, 2015
0.05 (medium dark color in each panel) and more than 0.05 (dark color in each panel). Change directions that indicate dominant atmospheric climate drivers (5) are highlighted in green (45°<θ<90° or 225°<θ<270).
Submitted Manuscript: Confidential October 31, 2015
Tables S1-S4
Table S1. Hydroclimatic conditions (1901-2008) and changes between the periods 1901-1954 and 1955-2008, FRI-impact level categorization and other relevant information for the 100 basins studied worldwide. This table includes the data reported in this paper and is tabulated as part of these Supplementary Materials.
GRDC Station River basin
FRI- impact
category
Latitude (°)
A
(km2) MPER1 MPER2 AET/P PET/P CVP CVR AET/P PET/P CVP CVR
AET
(mm/yr) LET
(mm/yr) RES
(mm) Vr
(km3) IA Mag
1537100 CHARI NA 12.1 600,000 15 33 0.95 1.57 1.09 1.00 0.01 0.12 -0.007 0.035 -67 -22 0 0.000 0.000
87
0.12
2908302 CHUNA (UDA) NA 54.1 17,200 19 35 0.34 0.50 0.97 1.05 -0.03 0.06 0.012 -0.017 -36 -35 0 0.000 0.000
118
0.07
4214610 HARRICANAW RIVER NA 48.6
3,680 22 44 0.41 0.41 0.45 0.72 0.07 -0.02 0.028 -0.023 78 74 0 0.000 0.000
341
0.07
2998400 INDIGIRKA NA 69.6 305,000 18 41 0.39 0.89 0.90 1.48 -0.09 0.10 -0.023 -0.014 -36 -29 0 0.000 0.000
129
0.14
6233850 KALIXAELVEN NA 66.2 23,103 18 54 0.20 0.62 0.50 0.94 0.00 -0.06 -0.074 0.008 7 5 0 0.000 0.000
269
0.06
2902800 KAMCHATKA NA 56.3 45,600 23 31 0.27 0.35 0.46 0.62 -0.03 -0.02 -0.055 -0.017 -6 -7 0 0.000 0.000
221
0.04
6970500 MEZEN NA 65.0 56,400 32 48 0.37 0.55 0.46 1.29 0.02 -0.01 -0.061 0.029 15 15 0 0.000 0.000
320
0.02
2999920 OLENEK NA 68.6 127,000 18 45 0.41 0.70 0.73 1.91 -0.07 0.06 -0.153 0.185 -33 -29 0 0.000 0.000
141
0.09
6970101 ONEGA NA 62.6 40,600 25 41 0.54 0.61 0.50 1.00 -0.01 -0.06 -0.088 -0.006 20 16 0 0.000 0.000
259
0.06
4149300 PASCAGOULA RIVER NA 31.0
17,068 24 54 0.65 0.73 0.50 0.89 0.01 -0.04 -0.041 -0.021 49 50 4 0.001 0.004
283
0.04
6970700 PECHORA NA 67.6 312,000 14 18 0.16 0.53 0.46 1.18 0.00 -0.07 -0.068 0.110 13 1 0 0.000 0.000
269
0.07
2999500 PUR NA 67.0 95,100 16 30 0.41 0.49 0.59 1.16 -0.02 -0.01 -0.114 0.065 -13 -7 0 0.000 0.000
205
0.02
3652455 RIO JEQUITINHONHA NA -15.9
67,769 18 42 0.80 1.32 1.05 0.89 -0.01 0.08 0.021 -0.008 -34 -31 41 0.052 0.008
97
0.08
4206250 SKEENA RIVER NA 54.6 42,200 15 52 -0.15 0.58 0.46 0.98 -0.01 -0.02 0.022 -0.003 -7 -15 1 0.000 0.000
254
0.02
6233910 TORNEAELVEN, TORNIONJOKI NA 66.0
33,930 44 54 0.24 0.63 0.50 1.00 -0.01 -0.05 -0.034 -0.050 6 2 0 0.000 0.000
264
0.05
2998100 YANA NA 69.8 216,000 17 27 0.49 0.94 0.95 1.54 -0.04 0.12 -0.085 -0.068 -26 -19 0 0.000 0.000
109
0.13
4203901 YUKON RIVER NA 60.7 19,400 11 52 -0.06 0.74 0.41 0.62 -0.07 0.06 -0.001 -0.177 -28 -24 0 0.000 0.000
139
0.10
Submitted Manuscript: Confidential October 31, 2015
GRDC Station River basin
FRI- impact
category
Latitude (°)
A
(km2) MPER1 MPER2 AET/P PET/P CVP CVR AET/P PET/P CVP CVR
AET
(mm/yr) LET
(mm/yr) RES
(mm) Vr
(km3) IA Mag
4148720 ALTAMAHA RIVER MA 31.7
35,224 23 54 0.72 0.85 0.48 0.79 -0.01 -0.03 -0.015 0.052 6 11 25 0.016 0.026
258
0.04
3629000 AMAZONAS MA -1.9 4,680,000 19 28 0.45 0.67 0.31 0.27 0.00 -0.01 0.003 -0.025 9 13 5 0.393 0.001
266
0.01
2906900 AMUR MA 50.6 1,730,000 13 39 0.65 0.59 1.05 0.83 -0.03 0.01 0.016 -0.028 -9 -14 44 1.398 0.020
168
0.03
1147010 CONGO MA -4.3 3,475,000 51 54 0.75 0.92 0.29 0.22 -0.01 0.00 0.009 0.010 -4 -11 0 0.000 0.000
196
0.01
4147600 DELAWARE RIVER MA 40.2
17,560 42 54 0.47 0.52 0.42 0.67 0.01 0.00 0.018 -0.062 19 14 51 0.017 0.003
353
0.01
4207900 FRASER RIVER MA 49.4 217,000 42 54 0.39 0.59 0.37 0.79 0.00 -0.02 0.002 -0.065 10 0 4 0.017 0.004
254
0.02
2998502 KOLYMA MA 62.4 99,400 22 45 0.33 0.62 0.79 1.35 0.00 -0.01 -0.002 -0.156 3 2 15 0.027 0.000
282
0.01
6983350 KUBAN MA 45.2 48,100 20 31 0.73 0.66 0.40 0.50 0.05 -0.01 0.012 -0.001 57 49 63 0.056 0.012
353
0.05
2903420 LENA MA 70.7 2,430,000 20 48 0.39 0.58 0.74 1.33 -0.04 0.01 -0.044 -0.047 -14 -14 15 0.669 0.000
172
0.04
6123100 LOIRE MA 47.4 110,000 54 25 0.68 0.86 0.44 0.75 0.00 -0.01 0.009 -0.020 7 3 4 0.008 0.055
263
0.01
2969100 MEKONG MA 16.5 391,000 30 37 0.49 0.74 0.92 0.97 0.08 -0.02 -0.012 -0.004 104 107 20 0.146 0.023
347
0.08
6421100 MEUSE MA 51.8 29,000 44 41 0.62 0.69 0.45 0.72 0.01 -0.02 -0.016 0.000 24 27 5 0.003 0.032
301
0.03
2912600 OB MA 66.6 2,949,998 25 45 0.72 0.67 0.51 0.87 -0.01 0.01 -0.043 -0.024 2 -3 0 0.015 0.001
141
0.01
6457010 ODER RIVER MA 52.8 109,729 54 39 0.75 0.94 0.48 0.42 -0.01 0.03 0.056 -0.090 -24 -16 7 0.014 0.003
115
0.03
1643100 OGOOUE MA -0.7 205,000 21 21 0.60 0.79 0.64 0.43 0.01 0.00 -0.010 0.024 25 25 0 0.000 0.000
352
0.01
3206720 ORINOCO MA 8.2 836,000 30 35 0.49 0.63 0.66 0.70 -0.02 0.02 -0.008 0.007 -70 -65 12 0.192 0.005
128
0.03
4243600 OUTARDES (RIVIERE AUX) MA 49.2
18,900 30 24 0.35 0.30 0.39 0.75 0.08 -0.01 0.063 -0.232 93 87 1300 0.455 0.000
353
0.08
4148300 PEE DEE RIVER MA 34.2 22,870 16 54 0.68 0.76 0.46 0.59 -0.02 -0.04 -0.041 0.004 7 4 17 0.007 0.001
243
0.05
4147900 POTOMAC RIVER MA 38.9 29,940 24 54 0.66 0.68 0.44 0.79 -0.03 -0.01 0.021 0.069 -29 -18 9 0.005 0.001
210
0.03
6435060 RHINE RIVER MA 51.8 160,800 54 54 0.52 0.63 0.40 0.35 -0.02 0.01 -0.020 0.002 -11 -21 7 0.022 0.013
147
0.02
3653352 RIO ITAJAI-ACU MA -26.9 11,803 15 37 0.37 0.67 0.47 0.51 -0.04 -0.03 -0.027 0.047 -21 -53 9 0.002 0.013
215
0.05
3653181 RIO JUQUIA MA -24.3 4,383 18 25 0.46 0.70 0.61 0.39 0.03 -0.05 -0.045 -0.118 122 74 21 0.002 0.004
304
0.06
3265601 RIO PARANA MA -32.7 2,346,000 49 38 0.83 1.04 0.57 0.22 -0.02 -0.05 0.000 -0.046 32 5 131 5.687 0.006
249
0.05
3653120 RIO RIBEIRA DO IGUAPE MA -24.6
12,450 12 52 0.68 0.69 0.55 0.39 -0.04 -0.04 -0.063 -0.022 49 -5 14 0.003 0.001
225
0.06
Submitted Manuscript: Confidential October 31, 2015
GRDC Station River basin
FRI- impact
category
Latitude (°)
A
(km2) MPER1 MPER2 AET/P PET/P CVP CVR AET/P PET/P CVP CVR
AET
(mm/yr) LET
(mm/yr) RES
(mm) Vr
(km3) IA Mag
1338050 SANAGA MA 3.8 131,520 11 25 0.73 0.77 0.73 0.84 0.00 -0.02 0.019 -0.035 25 25 60 0.146 0.000
283
0.02
6122300 SEINE MA 48.8 44,320 27 23 0.73 0.97 0.47 0.80 0.01 -0.06 -0.024 -0.112 27 24 17 0.014 0.046
277
0.06
6970251
SEVERNAYA DVINA (NORTHERN DVINA) MA 63.3
285,000 15 33 0.54 0.58 0.45 1.21 0.00 0.01 -0.058 0.013 -2 -2 0 0.000 0.000
72
0.01
4145080 SKAGIT RIVER MA 48.4 8,011 14 54 -0.39 0.33 0.68 0.38 -0.01 -0.01 0.012 -0.055 -34 -45 0 0.000 0.001
214
0.01
4147700 SUSQUEHANNA RIVER MA 40.3
62,419 54 54 0.51 0.58 0.40 0.82 0.00 -0.01 -0.002 -0.064 13 9 31 0.035 0.000
278
0.01
6458010 VISTULA (WISLA) MA 54.1
194,376 54 39 0.72 0.91 0.52 0.50 -0.01 -0.01 -0.011 -0.078 -5 -7 9 0.034 0.002
204
0.02
4149400 ALABAMA RIVER SA 31.5 56,895 24 25 0.63 0.70 0.48 0.77 0.00 -0.07 -0.056 -0.006 50 58 64 0.067 0.001
272
0.07
2917100 AMU DARYA SA 42.3 450,000 18 18 0.76 1.57 0.91 0.71 0.05 -0.01 0.087 0.088 22 20 0 0.000 0.076
354
0.05
6233650 ANGERMANAELVEN SA 63.2
30,638 46 54 0.21 0.54 0.49 0.55 0.02 -0.02 -0.033 -0.554 18 12 93 0.053 0.000
310
0.03
4149630 APALACHICOLA RIVER SA 30.7
44,548 32 54 0.68 0.76 0.48 0.56 0.01 -0.02 -0.047 0.023 15 27 45 0.037 0.065
294
0.02
4148410 BROAD RIVER (TRIB. SANTEE) SA 34.6
7,226 16 54 0.63 0.70 0.49 0.55 0.00 -0.04 -0.062 -0.024 33 34 13 0.002 0.001
272
0.04
4214263 CHURCHILL RIVER SA 55.5
212,000 26 54 0.76 0.68 0.62 0.17 -0.02 -0.03 0.072 -0.053 15 5 0 0.000 0.000
237
0.03
4152103
COLORADO RIVER (PACIFIC OCEAN) SA 36.0
444,703 20 54 0.90 1.90 0.40 0.24 0.01 0.01 0.018 0.099 6 3 79 0.655 0.014
40
0.02
4115200 COLUMBIA RIVER SA 45.6
613,830 54 54 0.48 0.93 0.44 0.55 0.04 -0.04 -0.016 -0.287 38 22 96 1.093 0.043
312
0.06
4147460 CONNECTICUT RIVER SA 42.0
25,019 26 54 0.47 0.43 0.37 0.76 -0.02 -0.01 0.038 -0.087 -5 -11 3 0.001 0.000
204
0.02
6742900 DANUBE RIVER SA 45.2 807,000 34 48 0.67 0.79 0.40 0.33 -0.02 -0.01 0.010 0.007 -5 -9 23 0.347 0.039
202
0.02
5204251 DARLING RIVER SA -33.7 647,200 13 53 1.00 2.22 0.64 1.03 0.00 -0.01 0.029 0.125 15 3 7 0.082 0.015
281
0.01
6973300 DAUGAVA SA 55.9 64,500 17 51 0.65 0.74 0.50 1.03 -0.01 -0.02 -0.067 -0.193 22 4 0 0.000 0.001
251
0.02
6978250 DON SA 47.5 378,000 49 30 0.86 1.09 0.48 0.88 0.03 0.01 -0.024 -0.792 24 12 0 0.000 0.009
29
0.03
6112090 DUERO SA 41.2 91,491 22 14 0.71 1.16 0.70 0.88 -0.01 -0.15 0.009 0.107 56 23 0 0.000 0.054
268
0.15
6226800 EBRO SA 40.8 84,230 19 38 0.74 1.03 0.55 0.68 0.09 -0.02 0.042 -0.011 88 64 46 0.072 0.082
348
0.09
6340110 ELBE RIVER SA 53.2 131,950 54 53 0.71 0.98 0.43 0.49 -0.01 0.01 0.001 -0.027 1 -9 17 0.041 0.008
133
0.02
Submitted Manuscript: Confidential October 31, 2015
GRDC Station River basin
FRI- impact
category
Latitude (°)
A
(km2) MPER1 MPER2 AET/P PET/P CVP CVR AET/P PET/P CVP CVR
AET
(mm/yr) LET
(mm/yr) RES
(mm) Vr
(km3) IA Mag
6695200 EUPHRATES SA 38.8 63,835 18 18 0.48 0.93 0.71 0.98 -0.02 0.09 0.050 -0.059 -39 -26 0 0.000 0.075
104
0.09
6125100 GARONNE SA 44.4 52,000 34 25 0.57 0.81 0.47 0.72 -0.01 -0.04 -0.005 -0.044 11 4 4 0.004 0.086
254
0.04
2856900 GODAVARI SA 16.9 299,320 52 20 0.71 1.45 1.26 1.51 -0.01 0.04 0.005 -0.001 -20 -16 62 0.346 0.112
102
0.04
4147502 HUDSON RIVER SA 43.7 2,051 47 54 0.36 0.40 0.42 0.77 -0.03 0.01 0.019 -0.030 -42 -46 0 0.000 0.000
158
0.03
6854700 KEMIJOKI SA 65.8 50,683 44 47 0.24 0.73 0.55 0.87 -0.01 -0.04 -0.057 -0.153 0 0 80 0.075 0.000
258
0.04
4147050 KENNEBEC RIVER SA 45.1
7,032 25 54 0.49 0.38 0.38 0.52 -0.03 -0.02 0.004 -0.148 -6 -5 0 0.000 0.009
217
0.03
4146110 KLAMATH RIVER SA 41.5 31,339 19 51 0.41 0.72 1.01 0.88 0.01 -0.02 0.034 0.025 26 6 86 0.050 0.023
288
0.02
2854300 KRISHNA SA 16.5 251,355 54 20 0.75 1.94 1.07 1.47 0.05 -0.09 0.009 0.132 77 44 82 0.380 0.169
300
0.10
6990700 KURA SA 40.1 178,000 25 29 0.82 1.04 0.53 0.50 0.02 0.02 0.003 -0.333 9 9 16 0.052 0.115
36
0.03
6233750 LULEAELVEN SA 65.8 24,924 54 41 0.06 0.42 0.47 0.59 0.10 -0.03 -0.051 -0.506 67 66 401 0.185 0.000
344
0.10
4127800 MISSISSIPPI RIVER SA 32.3
2,964,255 23 49 0.74 0.88 0.42 0.51 -0.01 -0.05 -0.018 -0.108 20 14 38 2.076 0.038
263
0.05
5304140 MURRAY SA -34.8 113,127 25 46 0.92 1.31 0.58 0.78 0.01 -0.05 -0.040 -0.007 47 22 70 0.147 0.093
281
0.05
3275990 NEGRO (ARGENTINIA) SA -40.4
95,000 27 37 0.39 1.49 0.80 0.49 0.09 0.05 -0.062 -0.130 33 47 445 0.783 0.010
30
0.11
6974150 NEMUNAS - NEMAN SA 55.1
81,200 47 53 0.66 0.84 0.51 0.61 0.03 0.01 -0.037 -0.158 23 16 0 0.000 0.001
22
0.03
1234150 NIGER SA 13.5 700,000 11 49 0.94 2.42 1.18 0.76 0.00 0.36 -0.034 0.166 -95 -21 3 0.041 0.004
90
0.36
1362600 NILE SA 24.0 3,612,000 54 30 0.97 2.03 0.74 0.82 0.00 0.03 -0.042 -0.471 -9 0 46 3.093 0.007
82
0.03
1159100 ORANGE SA -28.8 866,486 16 42 0.97 3.00 0.87 1.07 0.01 -0.01 -0.026 -0.458 11 5 24 0.380 0.003
311
0.02
3652890 PARAIBA DO SUL SA -21.8 55,500 21 48 0.68 0.83 0.79 0.58 0.09 0.00 -0.025 0.030 158 126 129 0.133 0.010
359
0.09
4147010 PENOBSCOT RIVER SA 45.2
16,633 52 54 0.39 0.40 0.39 0.72 -0.02 -0.01 0.008 -0.058 2 -10 19 0.006 0.005
205
0.02
6348800 PO SA 44.9 70,091 37 37 0.47 0.47 0.51 0.43 0.01 -0.01 0.000 0.038 33 27 11 0.014 0.206
303
0.02
6139100 RHONE SA 43.9 95,590 34 44 0.49 0.54 0.47 0.41 0.01 -0.01 -0.003 0.010 25 20 31 0.055 0.036
309
0.02
4351900 RIO GRANDE (US/MX BORDER) SA 25.9
450,902 21 46 0.99 2.65 0.76 0.97 0.01 -0.23 0.019 -0.051 35 11 32 0.263 0.023
273
0.23
4146281 SACRAMENTO RIVER SA 38.8
55,040 25 54 0.61 0.91 1.11 0.59 -0.04 -0.02 0.083 -0.212 -18 -24 127 0.129 0.117
206
0.04
4243400 SAGUENAY (RIVIERE) SA 48.6
73,000 41 39 0.31 0.34 0.45 0.46 0.04 -0.01 0.036 -0.254 49 42 10 0.013 0.000
351
0.04
Submitted Manuscript: Confidential October 31, 2015
GRDC Station River basin
FRI- impact
category
Latitude (°)
A
(km2) MPER1 MPER2 AET/P PET/P CVP CVR AET/P PET/P CVP CVR
AET
(mm/yr) LET
(mm/yr) RES
(mm) Vr
(km3) IA Mag
4146360 SAN JOAQUIN RIVER SA 37.7
35,058 25 54 0.80 1.48 1.16 0.69 0.03 0.00 0.050 -0.209 25 18 287 0.186 0.248
359
0.03
3651900 SAO FRANCISCO SA -10.0 622,520 16 40 0.85 1.42 0.96 0.52 0.04 -0.05 -0.008 -0.120 90 56 118 1.358 0.005
311
0.07
4148650 SAVANNAH RIVER SA 32.5
25,512 20 53 0.68 0.76 0.46 0.44 0.01 -0.08 -0.066 -0.136 71 65 278 0.131 0.009
278
0.08
1812100 SENEGAL SA 16.5 268,000 51 10 0.88 2.48 1.35 1.22 -0.01 -0.14 0.014 -0.018 22 -4 0 0.000 0.001
265
0.14
4143550 ST. LAWRENCE RIVER SA 45.0
773,892 19 54 0.65 0.58 0.33 0.09 -0.01 -0.01 0.016 0.002 3 -1 2 0.024 0.005
225
0.02
2916200 SYR DARYA SA 44.1 219,000 14 30 0.82 1.33 0.76 0.55 0.09 0.18 0.068 0.123 7 16 143 0.580 0.119
64
0.20
6113100 TEJO SA 39.7 59,167 42 14 0.70 1.48 0.76 1.16 0.01 -0.08 0.000 -0.332 34 8 0 0.000 0.039
276
0.08
6977100 VOLGA SA 48.8 1,360,000 37 28 0.69 0.70 0.41 0.84 0.03 0.01 -0.017 -0.428 19 14 16 0.413 0.002
17
0.03
1531700 VOLTA SA 6.2 394,100 15 29 0.92 1.61 0.91 0.96 0.01 0.04 0.009 -0.915 -32 -2 386 2.816 0.001
79
0.04
6337200 WESER SA 53.0 37,720 54 54 0.60 0.86 0.43 0.57 -0.01 -0.02 -0.018 -0.043 8 -5 3 0.002 0.032
232
0.02
2181900 YANGTZE RIVER (CHANG JIANG) SA 30.8
1,705,383 12 34 0.48 0.68 0.65 0.52 0.06 0.03 -0.002 0.008 30 47 84 2.651 0.071
25
0.06
2909150 YENISEY SA 67.5 2,440,000 19 45 0.49 0.52 0.62 1.15 -0.02 0.02 -0.058 0.036 -15 -16 140 6.348 0.000
137
0.03
1291100 ZAMBEZI SA -17.5 339,521 11 38 0.90 1.22 1.09 0.94 0.00 0.04 -0.049 -0.146 -38 -23 0 0.000 0.000
97
0.04
Submitted Manuscript: Confidential October 31, 2015
Table S2. Sensitivity analysis regarding the basin requirement of minimum number of years with complete runoff (R) data. The results shown include the arithmetic mean (μ) of changes between 1901-1954 and 1955-2008 in the ratio of actual evapotranspiration to precipitation (AET/P) and relative intra-annual variability of monthly runoff (CVR) for basins in each FRI-impact category (non-affected - NA, moderately affected - MA and strongly affected - SA), and significance test results (p<0.05; in bold and with an asterisk) for the one-sided differences between any of the NA, MA and SA basin categories, as explained further in Table S3. Results are also shown for the estimated component of absolute total volumetric AET that depends on flow regulation and irrigation (FRI) based on two different estimation methods, 1 and 2, and the mean result of these.
Minimum
number of years of available
runoff (R) data in each period
NA MA SA Total NA - MA NA -SA MA -SA
Method 1 (FRI-related
AET) (km3/yr)
Method 2 (FRI-related
LET) (km3/yr)
Mean (Methods 1
and 2) (km3/yr)
3 N 21 33 62 116 p-values
4,138 2,593 3,366 AET/P -0.027 -0.010 0.015 AETP 0.039* 0.004* 0.054
CVR 0.02 -0.03 -0.15 CVR 0.214 0.003* 0.012*
5 N 19 32 61 112 p-values
4,292 2,221 3,257 AET/P -0.020 -0.005 0.016 AETP 0.179 0.008* 0.026*
CVR 0.03 -0.02 -0.17 CVR 0.033* 0.014* 0.110
10 N 17 30 53 100 p-values
4,355 2,396 3,376 AET/P -0.017 0.000 0.013 AETP 0.115 0.004* 0.020*
CVR 0.002 -0.035 -0.123 CVR 0.060 0.016 0.100
15 N 16 29 51 96 p-values
4,571 2,163 3,367 AET/P -0.019 -0.007 0.009 AETP 0.170 0.017* 0.056
CVR 0.01 -0.02 -0.18 CVR 0.080 0.020* 0.115
Submitted Manuscript: Confidential October 31, 2015
Table S3. Statistical significance tests for one-sided differences between the distributions of AET/P, PET/P, CVR, and CVP between the FRI-impact categories NA, MA and SA. See Fig. S3 for what these variables represent. The table shows p-values for the one-sided Wilcoxon rank sum test (or Mann-Whitney test) applied to all possible combinations. Significant differences (p<0.05) are shown with an asterisk and bold font.
AET/P PET/P CVR CVP
NA - MA 0.115 0.625 0.060 0.981 NA -SA 0.004* 0.749 0.016* 0.994 MA -SA 0.020* 0.517 0.100 0.599
Submitted Manuscript: Confidential October 31, 2015
Table S4. (A). Statistical significance of an unpaired Wilcox rank sum test on the differences of the distributions of RES, IA, PET/P and PET/P between the five distribution subgroups G1, G2, G3, G4 and G5. See Figs. 3 and S4 for what these variables and subgroups represent. Significant differences (p<0.05) are shown with an asterisk and bold font. (B) Similar tests for the differences between the distributions of RES, IA, CVP
and CVP. Significant differences (p<0.05) are shown with an asterisk and bold font.
A
Combinations RES IA PET/P PET/P G1-G2 0.080 0.031* 0.040* 0.868 G1-G3 0.550 0.345 0.091 0.954 G1-G4 0.063 0.004* 0.007* 0.972 G1-G5 0.003* 0.007* 0.034* 0.823 G2-G3 0.967 0.979 0.785 0.793 G2-G4 0.197 0.162 0.282 0.808 G2-G5 0.003* 0.171 0.292 0.389 G3-G4 0.017* 0.005* 0.199 0.671 G3-G5 0.000* 0.005* 0.239 0.170 G4-G5 0.051 0.473 0.718 0.121
B
Combinations RES IA CVP CVP
G1-G2 0.158 0.633 0.379 0.590 G1-G3 0.001* 0.429 0.484 0.579 G1-G4 0.007* 0.840 0.164 0.329 G1-G5 0.007* 0.840 0.868 0.473 G2-G3 0.003* 0.303 0.590 0.505 G2-G4 0.061 0.899 0.222 0.215 G2-G5 0.033* 0.832 0.925 0.389 G3-G4 0.972 0.917 0.184 0.207 G3-G5 0.593 0.871 0.929 0.431 G4-G5 0.218 0.505 0.988 0.671
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